{"id":"W2164870423","doi":"10.1109/tbme.2006.889181","title":"Algebraic Multigrid Preconditioner for the Cardiac Bidomain Model","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Preconditioner; Multigrid method; Solver; Discretization; Bidomain model; Conjugate gradient method; Computer science; Applied mathematics; Iterative method; Algorithm; Computational science; Mathematical optimization; Mathematics; Partial differential equation; Mathematical analysis; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003946565,0.0004371464,0.000690894,0.0002579543,0.0003599807,0.0005097518,0.0006378523,0.0007062535,0.003822266],"category_scores_gemma":[0.0008904384,0.00020566,0.0005303462,0.0003165618,0.0004533875,0.0003641126,0.0008613467,0.001070111,0.001026933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004087721,"about_ca_system_score_gemma":0.00114364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007498555,"about_ca_topic_score_gemma":0.004708467,"domain_scores_codex":[0.9997148,0.00008876208,0.00001306464,0.00003448601,0.0001156296,0.00003319765],"domain_scores_gemma":[0.9996978,0.0001070686,0.000040315,0.00004655956,0.00008372429,0.00002449687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001005348,0.0000518199,0.0007984915,0.0001689561,0.00002387099,0.0002115269,0.0001261159,0.8760641,0.01900633,0.06891385,0.004652838,0.02988155],"study_design_scores_gemma":[0.00001110086,0.00001494629,0.0000995904,0.000003981952,0.00000220096,0.00002094174,0.000006146007,0.9928052,0.001293703,0.002427635,0.003310237,0.000004429438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01796298,0.000229678,0.971593,0.0002557386,0.00006762866,0.00008761001,0.0003813991,0.0005923356,0.008829555],"genre_scores_gemma":[0.4069464,0.0004375002,0.5786865,0.0001858748,0.00006325659,0.0004234017,0.001011635,0.0003034912,0.01194189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007498555,"threshold_uncertainty_score":0.0149098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115604875855024,"score_gpt":0.2581793517240558,"score_spread":0.2470233029655056,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}